Research Publications
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Publication Open Access Factors Impacting Consumers' Intention to Consume Coconut Ice Cream: A Case Study in Kurunegala District in Sri Lanka(SLIIT,Business School, 2024) Patabandi, O.P.V.H; Wijethunga, Y.M.P.MIn an era where consumer preferences are rapidly shifting towards healthier, more sustainable, and inclusive food options, coconut ice cream has emerged as a popular alternative, captivating healthconscious individuals and meeting the growing demand from dairyfree consumers. This study focused on analyzing how various factors such as how product attributes, advertising, subjective norms, health consciousness, trust on product and availability influence consumers' intention to consume coconut ice cream. A questionnaire survey was conducted through face-to-face interviews to collect primary data from a sample of 300 respondents across six Divisional Secretariats in the Kurunegala district. Data analysis was performed using confirmatory factor analysis via AMOS in SPSS 24 version. The results revealed that trust in the product, advertising, subjective norms, health consciousness and availability are the main factors influencing consumers' consumption intention regarding coconut ice cream. These findings offer valuable insights for manufacturers, competitors, marketers and policymakers seeking to address the evolving preferences of health-conscious and dairy-free consumers in the contemporary food industry.Publication Open Access Alcohol and Heart - Health Nexus in Lower MiddleIncome Countries: Cardiovascular Risks Study(SLIIT,Business School, 2024) Gamage,J; Oshini, A; Palliyaguru, D; Senarathne,B; Rajamanthri, L; Wichramarachchi, CCardiovascular diseases have become a growing concern in lower-middle-income countries, not only as a public health challenge but also as a critical management issue influencing workforce productivity, absenteeism, and organizational performance. Understanding how lifestyle factors such as alcohol consumption affect cardiovascular health is vital for developing sustainable workplace health strategies. This study examines the impact of alcohol consumption—classified as wine, beer, and spirits—on cardiovascular diseases while incorporating key determinants such as cholesterol, diabetes, obesity, and tobacco use within lower-middle-income countries. Using panel data from 170 countries between 1990 and 2019, this study applies fixed and random effects regression models to explore the relationship between alcohol consumption patterns and cardiovascular disease prevalence. The analysis integrates major lifestyle and health variables to assess their combined impact on national and workforce health outcomes. Findings reveal that alcohol consumption, particularly beer and spirits, has a significant positive association with cardiovascular disease risk, while cholesterol, obesity, and tobacco use further exacerbate these effects. Interestingly, diabetes prevalence demonstrated a negative relationship with cardiovascular disease within lower-middle-income countries. The results underscore the economic and managerial implications of unmanaged lifestyle risks that contribute to lost productivity and healthcare burdens. This study highlights the critical intersection between health behavior and management, suggesting that promoting responsible alcohol consumption and preventive health measures can enhance workforce well-being and productivity. The insights provide valuable guidance for human resource professionals, organizational leaders, and policymakers in designing evidence-based wellness programs, occupational health policies, and strategic interventions aimed at reducing cardiovascular risks in developing economiesPublication Open Access Income Diversification on Human Capital and Banks Financial Performance: Evidence from Sri Lanka(SLIIT,Business School, 2024) Dammika W.A.D.C; Anuradha P.A.N.SExisting empirical literature and theories on human capital fail to offer a conclusive explanation for its relationship with income diversification and bank financial performance. Consequently, this study fills the gap by investigating whether income diversification moderates the relationship between human capital and bank financial performance in Sri Lanka. This understanding of how income diversification influences the relationship between human capital and bank financial performance, benefits researchers, practitioners, and policymakers alike. This study employs a deductive research approach and quantitative methodology, analyzing panel data from 2010 to 2022 across a sample of 19 banks. Findings confirmed a significant relationship between human capital, income diversification, and bank financial performance. Thus, competencies, knowledge, and skills of bank's employees play a crucial role in determining its overall success. Additional results indicate that a skilled and capable workforce significantly contributes to a bank's ability to broaden its revenue sources, thereby strengthening its financial stability and resilience. However, it fails to identify a moderating impact of income diversification on the relationship between human capital and bank financial performance. Nonetheless, it underscores the crucial role of human capital in shaping bank financial performance and the strategies of income diversification adopted by banks. This study contributes to the existing literature by investigating the moderating role of income diversification on the relationship between human capital and bank financial performance within the Sri Lankan context, as an aspect often overlooked in previous studies that primarily focused on the direct effects of human capital and income diversification on bank performance.Publication Open Access Factors Influencing Urban Consumer Milk Powder Brand Preferences: A Case From Western Province of Sri Lanka(SLIIT, Business School, 2024) Wijesinghe,A.G.K.; Liyanage,D. Y. H.; Wijethunga, Y.M.P.M.Brand preference is an essential attribute to study in consumer behaviour. This study was focused on evaluating the factors contributing to milk powder brand preference among consumers in the Gampaha district. The objectives of this study were to identify the factors that influence milk powder brand preference and to identify the most preferred milk powder brand among local and imported milk powder brands in the market. A questionnaire survey was conducted through face-to-face interviews to gather primary data from a sample of 400 respondents covering five divisional secretariats in the Gampaha district of the Western Province. The data were analysed using confirmatory factor analysis in AMOS. The results of the study show that the factors that affect brand preference are brand availability, trust in brands, and subjective norms. When considering brand preference for the chosen two local brands and seven imported brands, it shows that 55.05% purchased both local and imported brands, while 38.03% purchased only the local brands. In the study population, 6.91% purchased only imported brands. These results will help milk brand manufacturers, investors, advertisers, relevant businesses, and the government implement the required product changes and quality improvements in the milk powder industry in the country.Publication Open Access QPred: A Lightweight Deep Learning-Based Web Pipeline for Accessible and Scalable Streamflow Forecasting(Tech Science Press, 2026) Makumbura, R.K; Wijesundara, H; Sajindra, H; Rathnayake, U; Kumar, V; Duraibabu, D; Sen, SAccurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges and loggers, while river water depth in the Aglar and Paligaad watersheds was converted to discharge using site-specific rating curves, resulting in a daily dataset of precipitation, river water level and discharge. Four DL architectures were trained, including vanilla Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), and evaluated using Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Root-Mean-Square-Error-Standard-Deviation Ratio (RSR), and Percentage Bias (PBIAS) metrics. Performance was watershed-specific, as the vanilla LSTM demonstrated the best generalisation for the Aglar watershed (R2 = 0.88, NSE = 0.82, RMSE = 0.12 during validation), while the GRU achieved the highest validation accuracy in Paligaad (R2 = 0.88, NSE = 0.88, RMSE = 0.49). All models achieved satisfactory to excellent performance during calibration (R2 > 0.91, NSE > 0.91 for both watersheds), demonstrating strong capability to capture streamflow dynamics. The highest performing models were selected and embedded into the QPred application. QPred was developed as a lightweight web pipeline, utilising Google Colab as the primary execution environment, Flask as the backend inference framework, Google Drive for artefact storage, and Ngrok for secure HTTPS tunnelling. A user-friendly front end utilises range sliders (bounded by observed minima and maxima) to gather inputs and provides discharge data along with metadata, thereby enhancing transparency. This work demonstrates that accurate, context-aware deep learning models can be delivered through low-cost, web-based platforms, providing a reproducible and scalable pipeline for hydrological applications in other watersheds and for practitioners. CopyrightItem Open Access An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, MChronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.Item Embargo Adaptive Voice Communication in Emotion-Aware Digital Companions(Institute of Electrical and Electronics Engineers Inc., 2025) Rathnayake, P; Rathnaweera, C; Jithma, U; Aththanayake, I; Rathnayake, S; Gunaratne, MThis paper presents an adaptive voice communication system for emotion-aware digital companions that dynamically responds to users' affective states through expressive speech and synchronized 3D avatar animation. The system integrates real-time voice input, emotion recognition, and context-aware dialogue generation using GPT-3.5, followed by emotional text-to-speech synthesis via neural TTS. Lip-sync data is generated using phoneme alignment and rendered in sync with the avatar's facial expressions and gestures. To enhance user trust and engagement, the avatar visually mirrors the emotional tone of the speech. A cultural adaptation layer is introduced to align voice output and speech style with Sri Lankan communication norms, including tone, pacing, and formality. Implemented using a Node.js backend and React + Three.js frontend, the system demonstrates strong potential for emotionally intelligent, culturally adaptive AI interactions. This work contributes a modular pipeline for building empathetic voice agents capable of enhancing realism and trust in human-AI communication.Item Embargo A Game Centric E-Learning Application For Preschoolers(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara D.A.M.N.; Nipun P.G.I.; Dombawela H.M.D.L.B.A; Manilka G.S; Manilka G.S; De Silva D.I.This research explores the potential of advanced technologies such as pose detection (PD), augmented reality (AR), object detection (OD), and voice recognition (VR) in creating a game-centric e-learning application for preschoolers. The proposed application, Kidstac, integrates cognitive and physical development through interactive activities with real world interaction, addressing gaps in traditional e-learning methods that often neglect physical engagement. The app features real-time feedback mechanisms and structured modules like virtual zoo explorations, exercise games, treasure hunts, and pronunciation activities. Testing results indicate significant improvements in motor skills, knowledge retention, problem-solving abilities, and language proficiency. These findings demonstrate the effectiveness of blending physical and digital learning experiences to enhance early childhood education. The study establishes a foundation for scalable, activity-based learning tools, emphasizing the holistic development of young learners.Item Embargo Predictive Modelling of Egg Production Yields on Farms based on Environmental Factors(Institute of Electrical and Electronics Engineers Inc., 2025) Nawod G.A.D; Rathnayake R.M.D.A.; Dodangoda P.N; Deshitha N.A.M.P; Vidanaralage A.J; Vidanaralage A.JThis research presents an integrated smart farming system aimed at optimizing egg yield on poultry farms by leveraging artificial intelligence (AI), Internet of Things (IoT), and environmental sensing technologies. The system is structured around four core components - Animal Stress Monitoring, Temperature Control and Predator Detection, Humidity and Ventilation Management, and AI-Driven Smart Lighting Optimization each contributing to real-time environmental adaptation and accurate egg production prediction. Animal stress is assessed using physiological and environmental metrics (e.g., heart rate, body temperature, feed/water intake), with predictions generated via an XGBoost model trained on 3000+ real farm entries. Temperature and security are managed through a hybrid system combining DHT11/DHT22-based climate control with YOLO-based computer vision for predator detection. The humidity and ventilation module incorporates Bi-LSTM and XGBoost models to predict and regulate airflow and moisture levels based on real-time sensor inputs. The lighting optimization component dynamically adjusts LED spectrum and intensity using LSTM-based forecasting models, operating via ESP32 and MQTT-enabled architecture to simulate ideal lighting conditions. These components are unified through a.NET-based backend and a mobile-friendly dashboard, enabling low-latency decision support and seamless farm management. The system's modularity, edge deployment capabilities, and adaptability to local conditions make it an innovative and scalable approach for enhancing egg yield, poultry welfare, and farm automation.Item Embargo The Impact of Interior Design Environment on Employee Satisfaction: An Insight on State Offices in Sri Lanka(Springer Science and Business Media Deutschland GmbH, 2025) Kalpani K.I.; Ratnayake J.C; Wimalaratna P.L.; Wijesundara JJob satisfaction is crucial in corporate settings, as it influences employees’ attitudes and performance. While previous studies have highlighted the importance of workplace conditions on job satisfaction across various countries, there is a notable lack of research within the Sri Lankan context, particularly in state offices. This research investigates the factors affecting employee satisfaction in Sri Lankan state offices, with a specific emphasis on interior design environment. The study aims to determine how specific interior design environmental cues impact employee satisfaction. Based on a comprehensive literature review, the independent variables identified include floor layout, furniture arrangement, lighting, colour scheme, air temperature, noise and acoustics. This study employs a mixed-method approach, combining quantitative and qualitative data, to explore the impact of the interior design environment on employee satisfaction in three high-profile state offices in Colombo and Sri Jayewardenepura. Primary data were collected through observations and structured questionnaires distributed across various departments, yielding 50 responses from each office, resulting in a total sample size of 150 participants. On-site measurements for lighting levels, temperature, and noise levels, were taken, while furniture, colour, and floor layout were assessed through visual inspections. Questionnaire responses were analysed using SPSS statistical software. The research found that floor layout, furniture, lighting, and colour significantly impact employee satisfaction, whereas temperature and noise have minimal impact. The study offers design recommendations to improve state office environments, emphasizing the importance of passive design techniques to enhance user-friendliness and environmental sustainability, ultimately increasing employee satisfaction. This research fills a critical gap in the literature and provides practical insights for improving the working conditions in Sri Lankan state offices.